28,976 research outputs found
The Effect of Liquidity on Governance
This paper studies the effect of stock liquidity on blockholders’ choice of governance mechanisms. We focus on hedge funds as they are unconstrained by legal restrictions and business ties, and thus have all governance channels at their disposal. Since the threat of governance, not just actual governance, can discipline managers, we use Section 13 filings to measure governance intent rather than only studying instances of actual governance. We find that liquidity increases the likelihood that a hedge fund acquires a block in a firm. Conditional upon acquiring a stake, liquidity reduces the likelihood that a blockholder governs through voice (intervention) – as evidenced by the greater propensity to file Schedule 13Gs (passive investment) rather than 13Ds (active investment). Liquidity is more likely to lead to a 13G filing if the manager’s wealth is sensitive to the stock price, consistent with governance through exit (trading). A 13G filing leads to positive announcement returns, especially in liquid firms. These two results suggest that liquidity does not dissuade blockholders from governing altogether, but instead encourages them to govern through exit rather than voice. We use decimalization as an exogenous shock to liquidity to identify causal effects.
Image Encryption Based on Diffusion and Multiple Chaotic Maps
In the recent world, security is a prime important issue, and encryption is
one of the best alternative way to ensure security. More over, there are many
image encryption schemes have been proposed, each one of them has its own
strength and weakness. This paper presents a new algorithm for the image
encryption/decryption scheme. This paper is devoted to provide a secured image
encryption technique using multiple chaotic based circular mapping. In this
paper, first, a pair of sub keys is given by using chaotic logistic maps.
Second, the image is encrypted using logistic map sub key and in its
transformation leads to diffusion process. Third, sub keys are generated by
four different chaotic maps. Based on the initial conditions, each map may
produce various random numbers from various orbits of the maps. Among those
random numbers, a particular number and from a particular orbit are selected as
a key for the encryption algorithm. Based on the key, a binary sequence is
generated to control the encryption algorithm. The input image of 2-D is
transformed into a 1- D array by using two different scanning pattern (raster
and Zigzag) and then divided into various sub blocks. Then the position
permutation and value permutation is applied to each binary matrix based on
multiple chaos maps. Finally the receiver uses the same sub keys to decrypt the
encrypted images. The salient features of the proposed image encryption method
are loss-less, good peak signal-to-noise ratio (PSNR), Symmetric key
encryption, less cross correlation, very large number of secret keys, and
key-dependent pixel value replacement.Comment: 14 pages,9 figures and 5 tables;
http://airccse.org/journal/jnsa11_current.html, 201
Least Squares Shadowing sensitivity analysis of chaotic limit cycle oscillations
The adjoint method, among other sensitivity analysis methods, can fail in
chaotic dynamical systems. The result from these methods can be too large,
often by orders of magnitude, when the result is the derivative of a long time
averaged quantity. This failure is known to be caused by ill-conditioned
initial value problems. This paper overcomes this failure by replacing the
initial value problem with the well-conditioned "least squares shadowing (LSS)
problem". The LSS problem is then linearized in our sensitivity analysis
algorithm, which computes a derivative that converges to the derivative of the
infinitely long time average. We demonstrate our algorithm in several dynamical
systems exhibiting both periodic and chaotic oscillations.Comment: submitted to JCP in revised for
An Adaptive Feature Extraction Algorithm for Classification of Seismocardiographic Signals
This paper proposes a novel adaptive feature extraction algorithm for
seismocardiographic (SCG) signals. The proposed algorithm divides the SCG
signal into a number of bins, where the length of each bin is determined based
on the signal change within that bin. For example, when the signal variation is
steeper, the bins are shorter and vice versa. The proposed algorithm was used
to extract features of the SCG signals recorded from 7 healthy individuals
(Age: 29.44.5 years) during different lung volume phases. The output of
the feature extraction algorithm was fed into a support vector machines
classifier to classify SCG events into two classes of high and low lung volume
(HLV and LLV). The classification results were compared with currently
available non-adaptive feature extraction methods for different number of bins.
Results showed that the proposed algorithm led to a classification accuracy of
~90%. The proposed algorithm outperformed the non-adaptive algorithm,
especially as the number of bins was reduced. For example, for 16 bins, F1
score for the adaptive and non-adaptive methods were 0.910.05 and
0.630.08, respectively
- …